EvalShift 发布 AI Agent 评测构建指南
Start here
2026 年 11 月 3 日，EvalShift 发布 AI Agent 评测构建指南，系统阐述了智能体全流程评测与传统单轮 LLM 评测的核心差异，并提出七步测试套件开发流程。指南指出，智能体评测必须重点校验外部环境状态与关键执行规则，避免模型仅凭顺畅回复掩盖未调用工具的缺陷。在落地实操方面，指南建议优先使用代码验证确定性规则，辅以校验过的 LLM Judge 评估文本质量，并采用 pass^k 指标衡量生产环境稳定性。
AI-generated from reports · Updated 1 day ago
Report timeline
Follow the reports to see different sides of the story.
- Hacker News · AISelectedHow to Write Evaluations for AI Agents: Core Concepts and a Seven-Step Guide
EvalShift has released a guide to building AI agent evaluations, systematically outlining the core differences between end-to-end agent evaluations and traditional single-turn LLM evaluations, alongside a seven-step workflow for developing test suites. The guide emphasizes that agent evaluations must rigorously verify external environment states and critical execution rules to prevent fluent text responses from concealing failures to invoke tools. In practice, it recommends prioritizing code assertions for deterministic rules, complemented by validated LLM-as-a-judge for text quality assessment, and using the pass^k metric to gauge production stability.
Story heat trend
Current heat 4·Comparable peak 10（Nov 3 05:00）·Comparable change, last 24 hours-50%
Trends compare only the same parties observed continuously, so the scope can be narrower than current heat counts. Move the pointer or tap the chart to see hourly heat; use the left and right arrow keys on a keyboard.